Mission: Data Self-Service Data Prep – how to become a professional Data Wrangler

In case you are involved with Big Data/Analytics initiatives from any standpoint, you have certainly noticed that the amount of time spent on Data Preparation tasks (aggregation, classification, cleansing, ingestions) can be overwhelming to both data analysts themselves and the leadership team at the company who issue their paychecks on monthly basis – putting the data into the right format can consume (analysts estimates) about 80% of analyst time.


At Software Connectors Asia we believe that thanks to paradigm of ‘Data Wrangling’, in its new Big Data contextual sense of meaning – amount of time suggested above can be significantly reduced and your team of data analysts/scientists can be more involved with other value-add initiatives and much needed data exploration itself.


Leading Data Prep vendor and our partner Trifacta coined the term ‘Data Wrangler’ and we will reflect today on have you could possibly get and claim that status: ‘Data Wrangling Professional’.


Our new citizen Data Wrangler will be active at the intersection of data analysis and its iteration cycle – iteration/optimization/getting the data right (preparing it) are important keywords in our exercise.


Newly certified data purveyor should not be in a vacuum though – data skills combined with the ability to interact with other stakeholders in the organization, like e.g. purchase pattern prediction analyst team and/or marketing campaign folks will help them not only to have a holistic view of data-related matters, deeper business purpose understanding – these might also enable our Data Wrangler to possibly enrich the data set in question.


Becoming a Data Wrangler brings plethora of new data citizen aptitudes:


You will be a better data analyst: data wrangling and its understanding will naturally teach you how to effectively cleanse challenging data sets,  recognize hidden data patterns and insights, embrace some of the predictive capabilities – and very importantly – share the outcomes of your data analysis in a clear and comprehensive manner.


Your developing skills will gain a new edge: there is certainly no shortage of new analytics technologies popping up as we speak, so apparently, it makes a lot of sense to master at least some of these, correct? Python, Kafka, Kudu, etc. you name it – benefits should be obvious.



You will improve your visual data story-telling skills: I do not think there is any elaboration needed on this – yes, GUI, design, everything visual is part of daily reality of 2016 and beyond, whether you like it or not, so you might as well benefit from this trend.


Become a better systems architect:  in times where companies call for holistic/comprehensive view of their data, it is also necessary to include all the data sources (to an extent possible) into the data systems analysis and architecture. Data Wrangling intrinsically brings about new thought process, which is different from traditional ETL, DW related paradigms in many aspects, so yes, your system architecture weaponry arsenal will be bigger and broader.


If interested to get to know more on how to become a ‘Data Wrangling Professional’, please kindly reach out to Software Connectors Asia (create necessary hyperlink) to discuss what are the available options.


Bruno Polach.

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